Intention-aware cooperative driving behaviour model for Automated Vehicles. This project aims to investigate humans' cooperation with automated systems by conceptualising joint intention awareness. This project expects to generate knowledge about a new cooperative driving behaviour model for automated vehicles, utilising a transdisciplinary approach that mixes human-centric methods with deep learning techniques. Intended outcomes are new joint intention awareness theory, new interface for automa ....Intention-aware cooperative driving behaviour model for Automated Vehicles. This project aims to investigate humans' cooperation with automated systems by conceptualising joint intention awareness. This project expects to generate knowledge about a new cooperative driving behaviour model for automated vehicles, utilising a transdisciplinary approach that mixes human-centric methods with deep learning techniques. Intended outcomes are new joint intention awareness theory, new interface for automated vehicles, new methodology for cooperative behaviour research, and enhanced research capacity. The expected significant benefits are for automated systems to become more predictable, acceptable, readable and safer to use by everyday people.Read moreRead less
A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Exp ....A human-centric eXplainable Automated Vehicle. The aim is to create a computational model to address the inability of Automated Vehicles (AV), powered by Artificial intelligence, to self explain their behaviours. This project applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable. Expected outcomes include the discovery of an acceptable, transparent and ethical explanation system that helps humans to understand the AVs decision making. This field will continue to rise in prominence and produce much-needed work to improve the widespread adoption of AVs.Read moreRead less
Engaging Augmented Reality on 3D Head Up Displays to Reduce Risky Driving. This project aims to reduce risky driving behaviours through novel augmented reality applications for three-dimensional head-up displays, making safe driving more engaging so that drivers will take less risk. Over 1 million people are killed and 50 million are seriously injured on roads each year worldwide. Risky driving behaviours (speeding and distracted driving) are major causes. This project intends to produce novel i ....Engaging Augmented Reality on 3D Head Up Displays to Reduce Risky Driving. This project aims to reduce risky driving behaviours through novel augmented reality applications for three-dimensional head-up displays, making safe driving more engaging so that drivers will take less risk. Over 1 million people are killed and 50 million are seriously injured on roads each year worldwide. Risky driving behaviours (speeding and distracted driving) are major causes. This project intends to produce novel in-car interaction design implementations, provide important visual design guidelines for future display technologies, and provide novel road safety interventions.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140101542
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Risky Gadgets to the Rescue: Designing Personal Ubicomp Devices to Foster Safer Driving Behaviours in Young Males. Young males are over-represented in road crashes. Part of the problem is their proneness to boredom, a hardwired personality factor that can lead to risky driving or distractions. This project aims to design innovative ubiquitous computing technologies that make safe driving more stimulating and pleasurable. This research will inform the future design of personal ubiquitous devices ....Risky Gadgets to the Rescue: Designing Personal Ubicomp Devices to Foster Safer Driving Behaviours in Young Males. Young males are over-represented in road crashes. Part of the problem is their proneness to boredom, a hardwired personality factor that can lead to risky driving or distractions. This project aims to design innovative ubiquitous computing technologies that make safe driving more stimulating and pleasurable. This research will inform the future design of personal ubiquitous devices that pose a threat to road safety, by replacing the stimuli from risky driving with safer stimuli and simulating risk to increase risk perception when it is actually not present. This project aims to reduce risky driving behaviours, and, in the process, advance our knowledge about the role of boredom in the road safety context.Read moreRead less
Coach My Ride: Mentorable Interfaces to support Older Australians' Mobility. This project aims to co-design new interfaces to support older Australians to collaboratively learn the use of automated vehicles. We will seek to understand the needs, expectations, and challenges of urban and rural residents, and the peer support strategies they deploy to learn technology. Mobility is key to the wellbeing of older people, but automated vehicles that are too complex will fail to deliver their promise o ....Coach My Ride: Mentorable Interfaces to support Older Australians' Mobility. This project aims to co-design new interfaces to support older Australians to collaboratively learn the use of automated vehicles. We will seek to understand the needs, expectations, and challenges of urban and rural residents, and the peer support strategies they deploy to learn technology. Mobility is key to the wellbeing of older people, but automated vehicles that are too complex will fail to deliver their promise of independent ageing. Outcomes will be a new theory of collaborative learning and new mentorable interfaces to allow older adults to mentor each other to access and use new mobility solutions. This will contribute to narrow the digital and mobility gap improving the independence, safety and wellbeing of ageing Australians.Read moreRead less
Real-time Analytics on Urban Trajectory Data for Road Traffic Management. This project aims to develop real-time analytics and data management capabilities that leverage large-scale urban trajectory data to provide road operators with real-time insights into population movements and enable data-driven, customer-centric network operations. Current traffic management practices rely heavily on aggregate vehicle count data from fixed road sensors, which have limitations in accurately measuring traff ....Real-time Analytics on Urban Trajectory Data for Road Traffic Management. This project aims to develop real-time analytics and data management capabilities that leverage large-scale urban trajectory data to provide road operators with real-time insights into population movements and enable data-driven, customer-centric network operations. Current traffic management practices rely heavily on aggregate vehicle count data from fixed road sensors, which have limitations in accurately measuring traffic demand and network congestion propagation. This project seeks to develop innovative technologies to use a wide variety of data sources, especially massive trajectories of vehicles moving across the network, to better understand people's travel demands and road usage patterns and thus better manage the transport system.
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